# alexnet.py """ AlexNet. References: - Alex Krizhevsky, Ilya Sutskever & Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. NIPS, 2012. Links: - [AlexNet Paper](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf) """ import tflearn from tflearn.layers.conv import conv_2d, max_pool_2d from tflearn.layers.core import input_data, dropout, fully_connected from tflearn.layers.estimator import regression from tflearn.layers.normalization import local_response_normalization def alexnet(width, height, lr): network = input_data(shape=[None, width, height, 1], name='input') network = conv_2d(network, 96, 11, strides=4, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 256, 5, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 384, 3, activation='relu') network = conv_2d(network, 256, 3, activation='relu') network = max_pool_2d(network, 3, strides=2) network = local_response_normalization(network) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 4096, activation='tanh') network = dropout(network, 0.5) network = fully_connected(network, 3, activation='softmax') network = regression(network, optimizer='momentum', loss='categorical_crossentropy', learning_rate=lr, name='targets') model = tflearn.DNN(network, checkpoint_path='model_alexnet', max_checkpoints=1, tensorboard_verbose=2, tensorboard_dir='log') return model